INTRODUCTION: With the large-scale integration of new energy into the grid, the safety and reliability of the power grid have been severely tested. The optimized configuration of micro power systems is a key element of intelligent power systems, playing a crucial role in reducing energy consumption and environmental pollution. OBJECTIVES: a power grid optimization scheduling model is proposed that comprehensively considers the issues of power grid operating costs and environmental governance costs METHODS: Using quantum particle swarm optimization method to optimize the objective function with the lowest system operating cost and the lowest environmental governance cost. In order to improve the search ability of the algorithm and eliminate the problem of easily getting stuck in local optima, the Levy flight strategy is introduced, and the variable weight method is used to update the particle factor to improve the optimization ability of the algorithm. RESULTS: The simulation results show that the improved quantum particle swarm optimization algorithm has strong optimization ability, and the scheduling model proposed in this paper can achieve good scheduling results in different scheduling tasks. CONCLUSION: （1）The improved particle swarm algorithm, in comparison to itspredecessor, boasts a greater degree of optimization accuracy, aswifter convergence rate, and the capability to avoid the algorithm'sdescent into the local optimal solution at a later stage of the process. （2）The proposed model can effectively reduce users’ electricity costs and environmental pollution, and promote the optimized operation of microgrids.
{"title":"Research on optimal scheduling of microgrid based on improved quantum particle swarm optimization algorithm","authors":"Fengyi Liu, Pan Duan","doi":"10.4108/ew.5696","DOIUrl":"https://doi.org/10.4108/ew.5696","url":null,"abstract":"INTRODUCTION: With the large-scale integration of new energy into the grid, the safety and reliability of the power grid have been severely tested. The optimized configuration of micro power systems is a key element of intelligent power systems, playing a crucial role in reducing energy consumption and environmental pollution. \u0000OBJECTIVES: a power grid optimization scheduling model is proposed that comprehensively considers the issues of power grid operating costs and environmental governance costs \u0000METHODS: Using quantum particle swarm optimization method to optimize the objective function with the lowest system operating cost and the lowest environmental governance cost. In order to improve the search ability of the algorithm and eliminate the problem of easily getting stuck in local optima, the Levy flight strategy is introduced, and the variable weight method is used to update the particle factor to improve the optimization ability of the algorithm. \u0000RESULTS: The simulation results show that the improved quantum particle swarm optimization algorithm has strong optimization ability, and the scheduling model proposed in this paper can achieve good scheduling results in different scheduling tasks. \u0000CONCLUSION: （1）The improved particle swarm algorithm, in comparison to itspredecessor, boasts a greater degree of optimization accuracy, aswifter convergence rate, and the capability to avoid the algorithm'sdescent into the local optimal solution at a later stage of the process. （2）The proposed model can effectively reduce users’ electricity costs and environmental pollution, and promote the optimized operation of microgrids.","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"45 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140721228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiaoming Zhang, Wen Cao, Yuhang Sun, Li Wang, Qi Chai
BACKGROUND: User-side distributed generation represented by distributed photovoltaic and distributed wind turbine has shown an expansion trend of decentralized construction and disordered access, which is difficult to satisfy the demand for large-scale exploitation and sustainable development of distributed generation under the low-carbon transformation vision of the power system. OBJECTIVES: To address the interest conflict and operation security problems caused by scaled distributed generation accessing the distribution network, this paper proposes the optimal planning method of user-side scaled distributed generation based on the Stackelberg game. METHODS: Firstly, a cluster planning and operation mode of distributed generation is established. Then, a prediction method for planning behavior of user-side distributed generation is proposed in order to predict whether users will adopt the self-build mode or the leasing site mode for distributed generation. Finally, in order to reveal the game relationship between the distribution network operator and the users in the allocation of distributed generation resources, a bi-level planning model for scaled distributed generation is established based on the Stackelberg game. RESULTS: The simulation results show that the revenue of the distribution network operator under the gaming model increases by 10.15% and 16.88% compared to the models of all users self-build distributed generation and all users leasing distributed generation site, respectively, while at the same time, individual users also realize different degrees of revenue increase. CONCLUSION: The case analysis validates the effectiveness of the proposed method in guiding the rational and efficient planning of user-side distributed generation.
{"title":"Optimal Planning of User-side Scaled Distributed Generation Based on Stackelberg Game","authors":"Xiaoming Zhang, Wen Cao, Yuhang Sun, Li Wang, Qi Chai","doi":"10.4108/ew.5655","DOIUrl":"https://doi.org/10.4108/ew.5655","url":null,"abstract":"BACKGROUND: User-side distributed generation represented by distributed photovoltaic and distributed wind turbine has shown an expansion trend of decentralized construction and disordered access, which is difficult to satisfy the demand for large-scale exploitation and sustainable development of distributed generation under the low-carbon transformation vision of the power system. \u0000OBJECTIVES: To address the interest conflict and operation security problems caused by scaled distributed generation accessing the distribution network, this paper proposes the optimal planning method of user-side scaled distributed generation based on the Stackelberg game. \u0000METHODS: Firstly, a cluster planning and operation mode of distributed generation is established. Then, a prediction method for planning behavior of user-side distributed generation is proposed in order to predict whether users will adopt the self-build mode or the leasing site mode for distributed generation. Finally, in order to reveal the game relationship between the distribution network operator and the users in the allocation of distributed generation resources, a bi-level planning model for scaled distributed generation is established based on the Stackelberg game. \u0000RESULTS: The simulation results show that the revenue of the distribution network operator under the gaming model increases by 10.15% and 16.88% compared to the models of all users self-build distributed generation and all users leasing distributed generation site, respectively, while at the same time, individual users also realize different degrees of revenue increase. \u0000CONCLUSION: The case analysis validates the effectiveness of the proposed method in guiding the rational and efficient planning of user-side distributed generation.","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"40 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140737958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
INTRODUCTION: The highway monitoring data acquisition technology develops quickly. Based on the traditional form of continuous monitoring, intelligent management system focuses on digital and wireless transmission. In the operation of highway maintenance system, each system is independent of each other, lacking of effective connection. Moreover, the level of continuous monitoring is obviously backward, which restricts the development of highway health monitoring. It is necessary to further study the level of integration to achieve the real-time tracking and the monitoring of highway’s healthy development. OBJECTIVES: This paper presents a highway health maintenance system based on digital twin technology, which intends to provide a solution for efficient, stable and automatic data transmission of the highway operation and maintenance management. METHODS: The output of the algorithm after the noise reduction effect is compared with the data containing the generated noise. The average number of nodes is set before running the algorithm to determine the actual length of the vertical position of the embedded sensor (calculating the position of two sensor nodes). The vertical length can be referred to the combined noise level formed and the combined test to determine the position. With the help of the overall data, it can be seen that the Kalman low-pass filtering algorithm can well describe the trend of the received signal and retain the key information in the received signal. RESULTS: It proves that the algorithm in this paper has fast calculation speed and high efficiency, and the basic working principle is simple. Thus, it is a good data denoising solution. CONCLUSION: The output in the paper ensures the data exchange and the update of the whole life cycle of highway, defines the digital twin entity model, and provides a reference for the establishment of information and data network.
{"title":"Embedded Highway Health Maintenance System Based on Digital Twin Superposition Model","authors":"Bijun Lei, Rui Li, Rong Huang","doi":"10.4108/ew.5654","DOIUrl":"https://doi.org/10.4108/ew.5654","url":null,"abstract":"INTRODUCTION: The highway monitoring data acquisition technology develops quickly. Based on the traditional form of continuous monitoring, intelligent management system focuses on digital and wireless transmission. In the operation of highway maintenance system, each system is independent of each other, lacking of effective connection. Moreover, the level of continuous monitoring is obviously backward, which restricts the development of highway health monitoring. It is necessary to further study the level of integration to achieve the real-time tracking and the monitoring of highway’s healthy development. \u0000OBJECTIVES: This paper presents a highway health maintenance system based on digital twin technology, which intends to provide a solution for efficient, stable and automatic data transmission of the highway operation and maintenance management. \u0000METHODS: The output of the algorithm after the noise reduction effect is compared with the data containing the generated noise. The average number of nodes is set before running the algorithm to determine the actual length of the vertical position of the embedded sensor (calculating the position of two sensor nodes). The vertical length can be referred to the combined noise level formed and the combined test to determine the position. With the help of the overall data, it can be seen that the Kalman low-pass filtering algorithm can well describe the trend of the received signal and retain the key information in the received signal. \u0000RESULTS: It proves that the algorithm in this paper has fast calculation speed and high efficiency, and the basic working principle is simple. Thus, it is a good data denoising solution. \u0000CONCLUSION: The output in the paper ensures the data exchange and the update of the whole life cycle of highway, defines the digital twin entity model, and provides a reference for the establishment of information and data network.","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"7 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140738578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
INTRODUCTION: Wind turbine gearbox fault diagnosis is of great significance for the safe and stable operation of wind turbines. The accuracy of wind turbine gearbox fault diagnosis can be effectively improved by using complete wind turbine gearbox fault data and efficient fault diagnosis algorithms.A wind turbine gearbox fault diagnosis method based on EMD-DCGAN method is proposed in this paper. OBJECTIVES: It can solve the problem when the sensor fails or the data transmission fails, it will lead to errors in the wind turbine gearbox fault data, which in turn will lead to a decrease in the wind turbine gearbox fault diagnosis accuracy. METHODS: Firstly, the outliers in the sample data need to be detected and removed. In this paper, the EMD method is used to eliminate outliers in the wind turbine gearbox fault data samples with the aim of enhancing the true continuity of the samples; secondly, in order to make up for the lack of missing samples, a data enhancement algorithm based on a GAN network is proposed in the paper, which is able to effectively perfect the missing items of the sample data; lastly, in order to improve the accuracy of wind turbine gearbox faults, a DCGAN neural network-based fault diagnosis method is proposed, which effectively combines the data dimensionality reduction feature of deep learning method and the data enhancement feature of generative adversarial network, and can improve the accuracy and speed of fault diagnosis. RESULTS and CONCLUSIONS: The experimental results show that the proposed method can effectively identify wind turbine gearbox fault conditions, and verify the effectiveness of the algorithm under different sample data conditions.
引言：风机齿轮箱故障诊断对风机的安全稳定运行具有重要意义。本文提出了一种基于 EMD-DCGAN 方法的风电齿轮箱故障诊断方法。目的：解决当传感器故障或数据传输故障时，会导致风力发电机组齿轮箱故障数据出现误差，进而导致风力发电机组齿轮箱故障诊断精度下降的问题。方法：首先，需要检测并去除样本数据中的异常值。本文采用 EMD 方法剔除风电齿轮箱故障数据样本中的异常值，旨在增强样本的真实连续性；其次，为了弥补样本缺失的不足，本文提出了一种基于 GAN 网络的数据增强算法，能够有效完善样本数据的缺失项；最后，为了提高风力发电机齿轮箱故障的准确性，提出了一种基于 DCGAN 神经网络的故障诊断方法，该方法有效地结合了深度学习方法的数据降维特性和生成对抗网络的数据增强特性，能够提高故障诊断的准确性和速度。结果与结论：实验结果表明，所提出的方法能有效识别风力发电机齿轮箱故障情况，并验证了算法在不同样本数据条件下的有效性。
{"title":"Fault diagnosis of gearboxin wind turbine based on EMD-DCGAN","authors":"Guangyi Meng, Yuxing An, Dong Zhang, Xudong Li","doi":"10.4108/ew.5652","DOIUrl":"https://doi.org/10.4108/ew.5652","url":null,"abstract":"INTRODUCTION: Wind turbine gearbox fault diagnosis is of great significance for the safe and stable operation of wind turbines. The accuracy of wind turbine gearbox fault diagnosis can be effectively improved by using complete wind turbine gearbox fault data and efficient fault diagnosis algorithms.A wind turbine gearbox fault diagnosis method based on EMD-DCGAN method is proposed in this paper. \u0000OBJECTIVES: It can solve the problem when the sensor fails or the data transmission fails, it will lead to errors in the wind turbine gearbox fault data, which in turn will lead to a decrease in the wind turbine gearbox fault diagnosis accuracy. \u0000METHODS: Firstly, the outliers in the sample data need to be detected and removed. In this paper, the EMD method is used to eliminate outliers in the wind turbine gearbox fault data samples with the aim of enhancing the true continuity of the samples; secondly, in order to make up for the lack of missing samples, a data enhancement algorithm based on a GAN network is proposed in the paper, which is able to effectively perfect the missing items of the sample data; lastly, in order to improve the accuracy of wind turbine gearbox faults, a DCGAN neural network-based fault diagnosis method is proposed, which effectively combines the data dimensionality reduction feature of deep learning method and the data enhancement feature of generative adversarial network, and can improve the accuracy and speed of fault diagnosis. \u0000RESULTS and CONCLUSIONS: The experimental results show that the proposed method can effectively identify wind turbine gearbox fault conditions, and verify the effectiveness of the algorithm under different sample data conditions.","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140739807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
INTRODUCTION: In recent years, China has been building extensive wind/solar power stations. During the construction and operation of land-based wind/solar power stations, deformation monitoring is an important method to investigate the station stability. OBJECTIVES: Therefore, this study uses Sentinel-1 data and time-series InSAR technology to monitor the deformation of photovoltaic and wind power stations in Qingyuan County. METHODS: InSAR technology obtains deformation rate maps in the radar line of sight (LOS) direction for a wide area around the power station sites. Since wind/solar power stations are mainly located in natural environments with relatively dense vegetation coverage, this paper proposes a SBAS-InSAR method integrated with spatiotemporal filtering to accurately extract the time series deformation over a large area. Based on the statistical characteristic difference between the deformation and the atmospheric delay, spatiotemporal filterings are applied to remove the atmospheric delay from the InSAR derived deformation results. RESULTS: The experimental results show that spatiotemporal filtering is an effective and fast method to remove atmospheric delay. CONCLUSION: The integration of BSAS-InSAR with spatiotemporal filtering has great potential applications in the deformation monitoring of land-based wind/solar power station sites, which is critical for the construction and operation of land-based wind/solar power stations.
{"title":"Research on Land-Based Wind/Solar Power Station Site Deformation Monitoring Based on SBAS-InSAR Technology","authors":"Junke Guo, Ling Liu, Yongfeng Zheng, Wei Cai, Zhijun Wang, Shangqi Wang","doi":"10.4108/ew.5656","DOIUrl":"https://doi.org/10.4108/ew.5656","url":null,"abstract":"INTRODUCTION: In recent years, China has been building extensive wind/solar power stations. During the construction and operation of land-based wind/solar power stations, deformation monitoring is an important method to investigate the station stability. \u0000OBJECTIVES: Therefore, this study uses Sentinel-1 data and time-series InSAR technology to monitor the deformation of photovoltaic and wind power stations in Qingyuan County. \u0000METHODS: InSAR technology obtains deformation rate maps in the radar line of sight (LOS) direction for a wide area around the power station sites. Since wind/solar power stations are mainly located in natural environments with relatively dense vegetation coverage, this paper proposes a SBAS-InSAR method integrated with spatiotemporal filtering to accurately extract the time series deformation over a large area. Based on the statistical characteristic difference between the deformation and the atmospheric delay, spatiotemporal filterings are applied to remove the atmospheric delay from the InSAR derived deformation results. \u0000RESULTS: The experimental results show that spatiotemporal filtering is an effective and fast method to remove atmospheric delay. \u0000CONCLUSION: The integration of BSAS-InSAR with spatiotemporal filtering has great potential applications in the deformation monitoring of land-based wind/solar power station sites, which is critical for the construction and operation of land-based wind/solar power stations.","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"36 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140740028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jing Niu, Chuanyan Shen, Jiapei Wei, Shifeng Liu, Cheng Lin
INTRODUCTION: To solve the problems of low quality and weak global optimization of the DWA algorithm, especially the problems of unreasonable path planning and the inability to give consideration to speed and driving safety in the process of vehicles passing through dense obstacles, this paper proposed an improved DWA algorithm based on ant colony algorithm. OBJECTIVES: The traffic capacity and computing efficiency of Self-driving Vehicles in complex dense obstacles can be greatly improved. METHODS: Through the obstacle density and distance information obtained by high-precision sensors on the vehicle, the speed objective function is updating in real time by using ant colony algorithm. And the maneuverability and safety performance of vehicles passing through are considering by the way. RESULTS: The experimental results show that this method can obviously improve the vehicle's traveling ability and uneven path planning in the case of dense obstacles, and the number of iterations of the algorithm is reduced by more than 16%. CONCLUSION: The improved DWA algorithm integrated with the ant colony algorithm can effectively improve the operating efficiency of the algorithm, reduce the distance the car must go around outside the obstacles, and improve Car driving safety. The effectiveness and universality of the improved DWA algorithm were verified through experiments.
简介：为解决DWA算法质量不高、全局优化能力弱的问题，特别是车辆通过密集障碍物过程中路径规划不合理、无法兼顾速度和行车安全等问题，本文提出了一种基于蚁群算法的改进DWA算法。目标：大幅提高自动驾驶汽车在复杂密集障碍物中的通行能力和计算效率。方法：通过车辆上高精度传感器获取的障碍物密度和距离信息，利用蚁群算法实时更新速度目标函数。同时考虑车辆通过时的机动性和安全性能。结果：实验结果表明，该方法能明显改善车辆的行驶能力和密集障碍物情况下的不均匀路径规划，算法迭代次数减少了 16% 以上。结论：与蚁群算法相结合的改进型 DWA 算法能有效提高算法的运行效率，减少汽车在障碍物外的绕行距离，提高汽车行驶的安全性。通过实验验证了改进的 DWA 算法的有效性和通用性。
{"title":"Path Planning of Self-driving Vehicles Combining Ant Colony and DWA Algorithms in Complex Dense Obstacles","authors":"Jing Niu, Chuanyan Shen, Jiapei Wei, Shifeng Liu, Cheng Lin","doi":"10.4108/ew.5651","DOIUrl":"https://doi.org/10.4108/ew.5651","url":null,"abstract":"INTRODUCTION: To solve the problems of low quality and weak global optimization of the DWA algorithm, especially the problems of unreasonable path planning and the inability to give consideration to speed and driving safety in the process of vehicles passing through dense obstacles, this paper proposed an improved DWA algorithm based on ant colony algorithm. \u0000OBJECTIVES: The traffic capacity and computing efficiency of Self-driving Vehicles in complex dense obstacles can be greatly improved. \u0000METHODS: Through the obstacle density and distance information obtained by high-precision sensors on the vehicle, the speed objective function is updating in real time by using ant colony algorithm. And the maneuverability and safety performance of vehicles passing through are considering by the way. \u0000RESULTS: The experimental results show that this method can obviously improve the vehicle's traveling ability and uneven path planning in the case of dense obstacles, and the number of iterations of the algorithm is reduced by more than 16%. \u0000CONCLUSION: The improved DWA algorithm integrated with the ant colony algorithm can effectively improve the operating efficiency of the algorithm, reduce the distance the car must go around outside the obstacles, and improve Car driving safety. The effectiveness and universality of the improved DWA algorithm were verified through experiments.","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"14 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140737575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
INTRODUCTION: Tower-type heliostat field is a new type of energy conversion, which has the advantages of high energy efficiency, flexibility and sustainability and environmental friendliness. OBJECTIVES: Through the research and improvement of the tower heliostat field to promote the development of solar energy utilization technology. METHODS: In this paper, we calculate and optimize the tower heliostat field by using single objective optimization, high-dimensional particle swarm algorithm and multiple group genetic algorithm. RESULTS: In this case of question setting, average annual optical efficiency is 0.6696; average annual cosine efficiency is 0.7564; annual average shadow occlusion efficiency is 0.9766; average annual truncation efficiency is 0.9975; average annual output thermal power is 35539.1747W; mean annual output thermal power per unit area is 0.5657W.The optimal solution after the initial optimization of the algorithm is that the total number of mirror fields is 6,384 pieces, and the average annual output power per unit area is 530.6W. CONCLUSION: The model of this paper can reasonably solve the problem and has strong practicability and high efficiency, but high dimensional particle swarm algorithm due to easily get local optimal solution, so can introduce the chaotic mapping to increase the randomness of the search space, improve the global search ability of the algorithm.
{"title":"Optimization design of heliostat field based on high-dimensional particle swarm and multiple population genetic algorithms","authors":"Yiwen Huang","doi":"10.4108/ew.5653","DOIUrl":"https://doi.org/10.4108/ew.5653","url":null,"abstract":"INTRODUCTION: Tower-type heliostat field is a new type of energy conversion, which has the advantages of high energy efficiency, flexibility and sustainability and environmental friendliness. \u0000OBJECTIVES: Through the research and improvement of the tower heliostat field to promote the development of solar energy utilization technology. \u0000METHODS: In this paper, we calculate and optimize the tower heliostat field by using single objective optimization, high-dimensional particle swarm algorithm and multiple group genetic algorithm. \u0000RESULTS: In this case of question setting, average annual optical efficiency is 0.6696; average annual cosine efficiency is 0.7564; annual average shadow occlusion efficiency is 0.9766; average annual truncation efficiency is 0.9975; average annual output thermal power is 35539.1747W; mean annual output thermal power per unit area is 0.5657W.The optimal solution after the initial optimization of the algorithm is that the total number of mirror fields is 6,384 pieces, and the average annual output power per unit area is 530.6W. \u0000CONCLUSION: The model of this paper can reasonably solve the problem and has strong practicability and high efficiency, but high dimensional particle swarm algorithm due to easily get local optimal solution, so can introduce the chaotic mapping to increase the randomness of the search space, improve the global search ability of the algorithm.","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"26 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140736789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
INTRODUCTION: Due to rapid economic development and continuous human activities, land use changes in the suburbs of metropolitan areas are drastic, which in turn affects the balance of ecosystem functions. Analyzing and predicting the ecological service value characteristics and trade-offs in rapidly urbanizing regions is of great significance for promoting high-quality regional development.AIM: This study attempts to reveal the trade-off and synergistic characteristics between the internal values of ecosystem services in suburban metropolitan areas under the influence of rapid urbanization.METHODS: Based on the patch-generating land use simulation (PLUS) model, simulated the land use changes in Xinzheng City under multiple scenarios in 2030, combined with methods such as equivalent factor method and spatial autocorrelation analysis，estimating, and predicting the ecosystem service value and its trade-off synergy relationship in Xinzheng City from 1980 to 2030.RESULTS: The value of ecosystem services in Xinzheng City continues to decline, hydrological regulation and soil conservation are the most important ecosystem service functions, under the scenario of farmland protection, ESV shows a stable growth trend. The synergistic relationship between various functions of ESV is significant, the Shizu Mountain National Forest Park, Shuangji River and other high agglomeration areas, as well as the Airport Economic Zone and Nanlonghu Town and other low agglomeration areas, all show a synergistic relationship, with only a portion of the southern side of the main urban area of Xinzheng showing a balancing relationship.CONCLUSIONS: Our findings can scientifically identify the environmental advantages of ecological sustainable development in Xinzheng City, and transform them into development advantages, providing provide strong technical support for the spatial ecological restoration and ecological security pattern construction of metropolitan suburbs.
{"title":"Dynamic Simulation and Tradeoffs and Synergies of Ecosystem Service Value in Metropolitan Suburbs Using the PLUS Model","authors":"Chaoyu Zhang, Qi Jia, Yijie Liu, Ke Li, Yanhong Gao, Zhuyu Zheng","doi":"10.4108/ew.5650","DOIUrl":"https://doi.org/10.4108/ew.5650","url":null,"abstract":"INTRODUCTION: Due to rapid economic development and continuous human activities, land use changes in the suburbs of metropolitan areas are drastic, which in turn affects the balance of ecosystem functions. Analyzing and predicting the ecological service value characteristics and trade-offs in rapidly urbanizing regions is of great significance for promoting high-quality regional development.AIM: This study attempts to reveal the trade-off and synergistic characteristics between the internal values of ecosystem services in suburban metropolitan areas under the influence of rapid urbanization.METHODS: Based on the patch-generating land use simulation (PLUS) model, simulated the land use changes in Xinzheng City under multiple scenarios in 2030, combined with methods such as equivalent factor method and spatial autocorrelation analysis，estimating, and predicting the ecosystem service value and its trade-off synergy relationship in Xinzheng City from 1980 to 2030.RESULTS: The value of ecosystem services in Xinzheng City continues to decline, hydrological regulation and soil conservation are the most important ecosystem service functions, under the scenario of farmland protection, ESV shows a stable growth trend. The synergistic relationship between various functions of ESV is significant, the Shizu Mountain National Forest Park, Shuangji River and other high agglomeration areas, as well as the Airport Economic Zone and Nanlonghu Town and other low agglomeration areas, all show a synergistic relationship, with only a portion of the southern side of the main urban area of Xinzheng showing a balancing relationship.CONCLUSIONS: Our findings can scientifically identify the environmental advantages of ecological sustainable development in Xinzheng City, and transform them into development advantages, providing provide strong technical support for the spatial ecological restoration and ecological security pattern construction of metropolitan suburbs.","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"63 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140739257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the field of modern weather prediction, the accurate classification is essential, impacting critical sectors such as agriculture, aviation, and water resource management. This research presents a weather forecasting model employing two influential classifiers random forest and technique based on gradient boosting, both implemented using the Scikit-learn library. Evaluation is based on key metrics including F1 score, accuracy, recall, and precision, with Gradient Boosting emerging as the superior choice for precipitation prediction. The study examines the performance of Random Forest Regression, Gradient Boosting Regression, and Radial Basis Function Neural Network in forecasting precipitation, drawing on prior research that demonstrated the superiority of the Random Forest algorithm in terms of accuracy and speed. Ensemble methods, particularly the Voting Classifier, a fusion of Random Forest and Gradient Boosting, outperform individual models, offering a promising avenue for advancing weather classification.
在现代天气预报领域，准确的分类至关重要，它影响着农业、航空和水资源管理等关键部门。本研究介绍了一种天气预报模型，该模型采用了两种有影响力的分类器随机森林和基于梯度提升的技术，这两种分类器均使用 Scikit-learn 库实现。评估基于关键指标，包括 F1 分数、准确率、召回率和精确度，其中梯度提升技术是降水预测的最佳选择。本研究借鉴了之前的研究，考察了随机森林回归、梯度提升回归和径向基函数神经网络在降水预测方面的性能，这些研究表明随机森林算法在准确性和速度方面都更胜一筹。组合方法，特别是投票分类器（随机森林和梯度提升的融合）的表现优于单个模型，为推进天气分类提供了一个前景广阔的途径。
{"title":"Prognostication of Weather Patterns using Meteorological Data and ML Techniques","authors":"Saksham Mathur, Sanjeev Kumar, Tanupriya Choudhury","doi":"10.4108/ew.5648","DOIUrl":"https://doi.org/10.4108/ew.5648","url":null,"abstract":"In the field of modern weather prediction, the accurate classification is essential, impacting critical sectors such as agriculture, aviation, and water resource management. This research presents a weather forecasting model employing two influential classifiers random forest and technique based on gradient boosting, both implemented using the Scikit-learn library. Evaluation is based on key metrics including F1 score, accuracy, recall, and precision, with Gradient Boosting emerging as the superior choice for precipitation prediction. The study examines the performance of Random Forest Regression, Gradient Boosting Regression, and Radial Basis Function Neural Network in forecasting precipitation, drawing on prior research that demonstrated the superiority of the Random Forest algorithm in terms of accuracy and speed. Ensemble methods, particularly the Voting Classifier, a fusion of Random Forest and Gradient Boosting, outperform individual models, offering a promising avenue for advancing weather classification.","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"8 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140739527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As one of the core equipment of the power grid, the operation status of transformers directly affects the stability and reliability of the power system. To accurately evaluate the remaining life of power grid transformers, a genetic algorithm is applied to optimize the Extreme Learning Machine based on digital twin technology. Then, considering changes in load rate, a residual life prediction model for power grid transformers is constructed. From the results, the error of the research method was within 2℃, with a maximum error of only 1.76℃. The research model converged with a fitness value of 0.04 at 150 iterations. It showed good predictive performance for hot spot temperatures under different load rates, with an average accuracy of 99.97%. Compared with backpropagation models and extreme learning machine models, the research method improved accuracy by 2.85% and 1.01%, respectively, with small and stable prediction errors. It verified the superiority of the research model, indicating that the research method can improve the accuracy of predicting the remaining life for power grid transformers. By monitoring the operation status of transformers in real-time, potential faults can be detected in a timely manner. The maintenance and replacement can be carried out in advance to avoid power outages caused by equipment damage. In addition, the research can provide reference for the planning and design of power systems, and support the stability and reliability of power systems.
{"title":"The residual life prediction of power grid transformers based on GA-ELM computational model and digital twin data","authors":"Xiangshang Wang, Chunlin Li, Jianguang Zhang","doi":"10.4108/ew.4896","DOIUrl":"https://doi.org/10.4108/ew.4896","url":null,"abstract":"As one of the core equipment of the power grid, the operation status of transformers directly affects the stability and reliability of the power system. To accurately evaluate the remaining life of power grid transformers, a genetic algorithm is applied to optimize the Extreme Learning Machine based on digital twin technology. Then, considering changes in load rate, a residual life prediction model for power grid transformers is constructed. From the results, the error of the research method was within 2℃, with a maximum error of only 1.76℃. The research model converged with a fitness value of 0.04 at 150 iterations. It showed good predictive performance for hot spot temperatures under different load rates, with an average accuracy of 99.97%. Compared with backpropagation models and extreme learning machine models, the research method improved accuracy by 2.85% and 1.01%, respectively, with small and stable prediction errors. It verified the superiority of the research model, indicating that the research method can improve the accuracy of predicting the remaining life for power grid transformers. By monitoring the operation status of transformers in real-time, potential faults can be detected in a timely manner. The maintenance and replacement can be carried out in advance to avoid power outages caused by equipment damage. In addition, the research can provide reference for the planning and design of power systems, and support the stability and reliability of power systems.","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"9 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140744411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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